Alternating Direction Method of Multipliers for Machine Learning
Lin, Zhouchen.
Alternating Direction Method of Multipliers for Machine Learning [electronic resource] / by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2022. - XXIII, 263 p. 1 illus. online resource.
Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions.
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
9789811698408
10.1007/978-981-16-9840-8 doi
Machine learning.
Mathematical optimization.
Computer science--Mathematics.
Mathematics--Data processing.
Machine Learning.
Optimization.
Mathematical Applications in Computer Science.
Computational Mathematics and Numerical Analysis.
Q325.5-.7
006.31
Alternating Direction Method of Multipliers for Machine Learning [electronic resource] / by Zhouchen Lin, Huan Li, Cong Fang. - 1st ed. 2022. - XXIII, 263 p. 1 illus. online resource.
Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions.
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
9789811698408
10.1007/978-981-16-9840-8 doi
Machine learning.
Mathematical optimization.
Computer science--Mathematics.
Mathematics--Data processing.
Machine Learning.
Optimization.
Mathematical Applications in Computer Science.
Computational Mathematics and Numerical Analysis.
Q325.5-.7
006.31